Training data and the annotation approach are the two focal points of any supervised method. A model can be built from pre-annotated data or using de novo annotations. There are many pre-annotated and publicly available corpora such as CoNLL-2003 [1]}, GENIA [2]}, and BioCreative II [3]}. However, having such data leaves us with two challenges: (i) no training dataset can be found for many of the domain-specific entity classes in enterprise corpora due to data privacy concerns, IP restrictions, and problem specific use cases (needing de novo annotations), and (ii) data sparsity, which hinders the performance of supervised models on unseen data (needing incrementally augmented annotations to the annotated sentence pool).
